Knowledge Extraction & Ingestion
Design and implement automated extraction pipelines for technical tailings storage facility (TSF) documents (PDFs, scanned reports, tables, figures).
Develop validation and normalization logic to ensure extracted knowledge meets quality and consistency requirements.

Knowledge Schema & Ontology Design
Design and evolve domain ontologies and knowledge schemas to support structured storage of TSF, risk, asset, and operational data.
Implement schemas using RDF/OWL, including classes, properties, constraints, and semantic relationships.
Align schemas with industry standards and internal MAV data models.

Knowledge Graph Development
Build and manage RDF-based knowledge graphs in graph repositories (e.g., GraphDB, RDFox, Neptune, or equivalent).
Implement ingestion workflows that map extracted content into graph structures with traceability to source documents.
Support versioning, provenance, and evidence linking within the knowledge graph.

Retrieval & GraphRAG Pipelines
Design and implement graph-native retrieval pipelines,



combining SPARQL queries, reasoning, and embeddings where appropriate.
Develop GraphRAG architectures that leverage structured graph context rather than flat text retrieval.
Enable natural-language querying over the knowledge graph for downstream AI assistants and analytics tools.

Collaboration & Integration
Work closely with development team, domain experts, and AI engineers to refine extraction logic and schema requirements.
Support integration with cloud AI services (e.g., Azure AI, OpenAI models, document processing services).
Proven experience as a Knowledge Engineer, Ontology Engineer, or Knowledge Graph Engineer.
Robust understanding of RDF, OWL, SPARQL, and semantic data modeling.
Hands-on experience with RDF-based graph repositories (GraphDB, RDFox, Apache Jena, Neptune, etc.).
Experience designing automated knowledge extraction pipelines using LLMs.
Familiarity with visio

📌 Engineer Ai Bengaluru
🏢 Wsp
📍 Bengaluru